docstr: Predict whether the instances in `x` are outliers or not.
  
  Scores the instances in `x` and if the threshold was inferred, returns the outlier labels and p-values as well.
  
  Parameters
  ----------
  x
      Data to predict. The shape of `x` should be `(n_instances, n_features)`.
  
  Returns
  -------
  Dictionary with keys 'data' and 'meta'. 'data' contains the outlier scores. If threshold inference was          performed, 'data' also contains the threshold value, outlier labels and p-vals . The shape of the scores is         `(n_instances,)`. The higher the score, the more anomalous the instance. 'meta' contains information about         the detector.
  
  Raises
  ------
  NotFittedError
      If called before detector has been fit.
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code: def predict(self, x: np.ndarray) -> Dict[str, Any]:
      """Predict whether the instances in `x` are outliers or not.
  
          Scores the instances in `x` and if the threshold was inferred, returns the outlier labels and p-values as well.
  
          Parameters
          ----------
          x
              Data to predict. The shape of `x` should be `(n_instances, n_features)`.
  
          Returns
          -------
          Dictionary with keys 'data' and 'meta'. 'data' contains the outlier scores. If threshold inference was  \
          performed, 'data' also contains the threshold value, outlier labels and p-vals . The shape of the scores is \
          `(n_instances,)`. The higher the score, the more anomalous the instance. 'meta' contains information about \
          the detector.
  
          Raises
          ------
          NotFittedError
              If called before detector has been fit.
          """
      outputs = self.backend.predict(self.backend._to_backend_dtype(x))
      output = outlier_prediction_dict()
      output["data"] = {
          **output["data"],
          **self.backend._to_frontend_dtype(outputs),
      }
      output["meta"] = {
          **output["meta"],
          "name": self.__class__.__name__,
          "detector_type": "outlier",
          "online": False,
          "version": __version__,
      }
      return output
  
file_path: alibi-detect/alibi_detect/od/_svm.py
imports: from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple
  import numpy
  from alibi_detect.base import BaseDetector, FitMixin, ThresholdMixin, outlier_prediction_dict
  from alibi_detect.exceptions import _catch_error
  from alibi_detect.od.pytorch import SgdSVMTorch, BgdSVMTorch
  from alibi_detect.utils._types import Literal
  from alibi_detect.utils.frameworks import BackendValidator
  from alibi_detect.version import __version__
  from alibi_detect.utils._types import TorchDeviceType
label_desc: Use image node_id 5 for calling the SVM obj's underlying member method code with example usage: obj.predict(x) and returns: output